Optical manufacturing process sensing and status indication system

ABSTRACT

An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/678,945, filed Nov. 8, 2019; which is a continuation of U.S.application Ser. No. 16/434,577, filed Jun. 7, 2019, now U.S. Pat. No.10,520,372 issued on Dec. 31, 2019; which is a continuation of U.S.patent application Ser. No. 15/276,452, filed Sep. 26, 2016; now U.S.Pat. No. 10,317,294 issued on Jun. 11, 2019; which is a continuation ofand claims priority to PCT/US2015/022539, filed Mar. 25, 2015; whichclaims the benefit of U.S. Provisional Patent Application No.61/970,407, filed Mar. 26, 2014. The disclosures of each are herebyincorporated by reference in their entirety for all purposes.

DESCRIPTION OF INVENTION

For manufacturing processes that involve the addition of heat atrelatively fast timescales and the removal of heat through conductionand other mechanisms such as convection and radiation at relativelyslower timescales, it is desirable to have a process sensing and processstatus indication system that can independently and separately compareif the heat input as well as the material response is largely similar orvery different between two different processing conditions. Forprocesses that get relatively hot, i.e. above 700 C, there is observableoptical radiation from the process which can serve as the basis of asensing mechanism. This can form the basis of an in-process qualityassurance methodology that can use the in-process data from both thefaster heat input processes as well as the slower heat dissipationprocesses to determine if a given process condition is largely similarto the desired baseline process condition, or if it is very differentfrom this desired baseline process condition.

Embodiments of the present invention can enable an optical sensingsystem to identify, examine, and analyze features associated with a heatsource from two different process conditions and determine if they arelargely similar or very different. Embodiments of the present inventioncan also identify, examine, and analyze features associated with thematerial response to a heat input associated with two different processconditions to determine if they are largely similar or very different.Embodiments of the present invention can also determine if the featuresfrom the faster timescale heat input and associated with a given processcondition are largely similar to those associated with a known baselineprocess condition and therefore considered nominal or very differentfrom that baseline condition and therefore considered off-nominal.Embodiments of the present invention can determine if process featuresfrom the slower material response to the heat input and associated witha given process condition are largely similar to those associated with aknown baseline process condition and therefore considered nominal orvery different from that baseline condition and therefore consideredoff-nominal.

There are many examples of manufacturing processes involving theaddition of heat on a relatively rapid timescale and the response of thematerial over a relatively slower timescale. For example, consider theautogenous welding of a part (i.e. no addition of material) comprising ascanning heat source moving rapidly over the joint between the two metalsurfaces to be joined. Assume that the diameter of the heat source is Dand the travel speed of the heat source is V. Therefore, thecharacteristic time of interaction between the heat source and thepieces of metal to be joined is described by the following equation:

$\begin{matrix}{t_{{heat}\mspace{14mu}{source}} = \frac{D}{V}} & (1)\end{matrix}$

It is seen that for very small D and very large V, this characteristictime of interaction of the heat source with the workpiece can be verysmall. For example, in laser powder bed additive manufacturing processeslike direct metal laser sintering (DMLS) and SLS (selective lasersintering), the diameter of the heat source can be 0.1 mm or smaller,and the scanning speed of the heat source can be 1000 mm/s or faster,and therefore the characteristic interaction time of the heat sourcewith the workpiece can be much less than 1 ms and could be close to 100microseconds.

With respect to the material response, for the aforementioned laserpowder bed processes, the material response will be dominated by heatconduction. The characteristic time for heat conduction is given by thefollowing equation:

$\begin{matrix}{t_{{{material}\mspace{14mu}{response}}\;} = \frac{X^{2}}{4\alpha}} & (2)\end{matrix}$

Where X is a characteristic length over which heat conduction occurs,and □ is the thermal diffusivity of the material. For many metals, thethermal diffusivity is on the order of 10-8 m2/s. If we take the samecharacteristic distance, i.e. the beam diameter which is assumed to be100 microns, then the characteristic time for the material response ison the order of 0.25 seconds. Therefore, it is seen that the fastercharacteristic time is 1000 to 10,000 times faster than the slowercharacteristic time. This means that the timescale over which heat inputinto the process is 100 to 10,000 faster than the timescale over whichthe material is able to dissipate this heat. In other words, the heatinput characteristic time is significantly faster than the materialsresponse time.

This is a universally observed phenomenon for rapid thermal processessuch as laser welding, electron beam welding, and even many arc weldingprocesses where the travel speed of the arc is high or the energydensity of the arc is high. This is also a prevalent process conditionfor many additive manufacturing processes that are based on lasers andelectron beams and involve the addition of material either through asequentially layered and melted or sintered powder bed or the directaddition of material through wire fed by a mechanical wire feeder orpowder being fed by a nozzle where the powder may by fluidized, carried,or otherwise entrained in an inert gas stream.

Embodiments of the present invention can measure these two verydifferent process timescales associated with a manufacturing processwhere thermal energy is added rapidly by a scanning external heatsource. Embodiments of the present invention can derive process featuresfrom these thermal data gathered on these two very different timescales.

For manufacturing processes that add heat and that achieve significanttemperatures such that there are radiative emissions coming from theprocess, another mode of sensing is spectroscopy. The radiation that isthus emitted is both blackbody radiation as well as characteristicradiation. The blackbody radiation is a function of the temperature ofthe process and is governed by Planck's Equation:

$\begin{matrix}{{I\left( {v,T} \right)} = {\frac{2{hv}^{3}}{c^{2}}\frac{1}{e^{\frac{hv}{kT}} - 1}}} & (3)\end{matrix}$

Where I is the intensity of radiation emitted per unit time per unitarea normal to the emitting surface, T is the absolute temperature ofthe surface in degrees Kelvin, h is the Planck constant, k is theBoltzmann Constant, c is the Speed of Light, and □ is the frequency ofthe radiation emitted. The relationship between the absolute temperatureand the wavelength of the maximum emitted radiation is given by Wien'sDisplacement Law:

$\begin{matrix}{\lambda_{\max} = \frac{b}{T}} & (4)\end{matrix}$

Where b is a constant.

In addition to the blackbody radiation which is a function oftemperature, there are characteristic radiation peaks in the spectraldata which are due to photons emitted as a result of specific electronictransitions between quantum states of atoms excited by multi-photonprocesses or by direct electron collisions. These are calledcharacteristic because they are characteristic to a particular atom andionization state(s). These will manifest in the spectral data asspecific peaks at specific wavelengths. There will often be multiplepeaks associated with various elements and electronic transitions, andas a result this spectral data can quickly get very complex.

Specifically with respect to additive manufacturing as performed byeither laser or electron beam sintering of powders, the opticalradiation is a by-product of the interaction between the beam and thepowder bed. At lower energy fluences, where the fluence is defined aswatts of incident energy absorbed per unit area, the powders may notmelt completely and due to the fact that the powders have very poorthermal conductivity, the top surfaces of the powders can heat up andcause a larger amount of optical radiation even when there is nomelting. At very high fluences where there is significant melting, theincreased power can cause excessive liquid temperatures as well asexcessive vaporization, thus leading to increased optical emissions. Inthe middle range of fluences which is neither too high nor too low, theoptical emission can undergo a local minimum due to the fact that theenergy is optimally coupled to the powder bed, i.e. there is notexcessive surface heating due to poor thermal conductivity nor is thereexcessive liquid heating and vaporization.

If the integration times of the spectrometer as well as the incidentbeam time of the laser were absolutely identical and the spectrometeralways saw optical emissions for a given fixed time when the laser waswithin the field of view of the spectrometer, and if the same chemicalspecies were present in the vaporized plume in the same atomicpercentages, then the absolute value of the intensity of the opticalemission at a given characteristic spectral line could be used as anindicator of energy coupling to the powder bed. However the followingintervening factors prevent the use of the absolute value of spectralpeaks as a feature that could be used to gauge the extent of energycoupling between the incident energy beam and the powder bed during anadditive manufacturing process:

The spectrometer will have a certain finite field of view over a certainregion of the powder bed. Depending on the scan pattern of the laser orelectron beam, the beam will intersect this fixed field of view fordifferent periods of time. Thus, during a fixed shutter open time (alsoequal to the spectrometer integration time), the laser or electron beamwill not intersect the spectrometer field of view in the same way or thesame number of times. This will result in variations in the intensitysignal.

The atomic concentration of species that are either excited neutralspecies or ionized and are giving off characteristic radiation will varyas a function of the power level. Also, in a multi-element,multi-component alloy, there could be several elements contributing tothe spectral lines and several of these elements may have closely spacedlines, especially for transition metals found in most common engineeringalloys that have complex electronic transition states and hence complexassociated spectra. This complex variation of atomic species that areresponsible for the characteristic emissions—both in terms of atomiccomposition and relative atomic percent—results in variations in theabsolute value of the spectral intensity at any given wavelength whichmakes it difficult to utilize this absolute value as a feature.

As a result of these and possibly other intervening factors, it isdesirable to select another feature that could allow the characteristicspectral data to be used as a discriminator to see when the energycoupling between the laser or electron beam and the powder bed may beoptimal. The FFT—Fast Fourier Transform—of the spectral data willindicate where any given spectrum is undergoing more rapid change invalue. When there is a greater atomic concentration of a given excitedneutral or ionized species in the plume above the energy beam/powder bedinteraction zone and these species are emitting characteristicradiation, it is expected that the corresponding spectral peaks will besharper and will therefore have higher values of FFT intensity at agiven inverse wavelength. Conversely, when the characteristic emissionsare lower due to the fact that there are relatively fewer excitedspecies due to a more optimal energy coupling, then the relative peak ata given wavelength will be broader and the background blackbodyradiation will play a more dominant role in the spectral intensity atthat given wavelength. Therefore the FFT intensity peak under suchconditions at the same inverse wavelength would be lower than thatobserved otherwise. Therefore the FFT intensity at some intermediateinverse wavelength (which will depend on the alloy composition) canserve as an indicator of the relative coupling efficiency of theincident energy beam to the workpiece.

Embodiments of the present invention can utilize spectrometry data todetermine relative energy coupling efficiency between an incident energybeam and a workpiece in an additive manufacturing process. Embodimentsof the present invention can determine the relative coupling efficiencyby utilizing the FFT—the Fast Fourier Transform—of the spectral data asa distinguishing feature that will allow such a classification of alocal optimum in energy coupling efficiency. Embodiments of the presentinvention can utilize the FFT feature thus derived to determine a localminimum in this same FFT signal that indicates a condition in which thecoupling between the energy source and the workpiece. In the case of anadditive manufacturing process that utilizes a powder bed and a laser orelectron beam to build up parts layer by layer. Embodiments of thepresent invention can utilize the FFT feature and more specifically alocal minimum in the FFT feature to determine the conditions under whichthe energy coupling between the laser or electron beam and the powderbed is optimal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a heat source impinging upon aworkpiece.

FIG. 2 is a schematic illustration of a workpiece giving off opticalradiation due to heating by a heat source and the optical sensor is anon-contact pyrometer.

FIG. 3 is a schematic illustration of raw pyrometer signals, which arethen transformed by extracting features therefrom.

FIG. 4 is a schematic illustration of an optical sensor that iscapturing the optical emissions from the radiation process, comprising aspectrometer where the raw spectrum shows integrated spectral intensityas a function of wavelength.

MODES OF CARRYING OUT THE INVENTION, AND INDUSTRIAL APPLICABILITY

In FIG. 1 , a heat source 100 is shown impinging upon a workpiece 101.There is a molten region 102 on the workpiece directly below the regionof the workpiece 101 affected by the energy source 100. There also couldbe a region of ionized or vaporized material 103 which is above themolten region 102. Both the molten region 102 and the vaporized orionized region 103 will emit optical radiation 104. This radiation isdetected by an optical sensor 105. The sensor can be stationary withrespect to the energy source, also known as an Eulerian reference frame,or it can be in the same reference frame as the moving energy source,also known as a Lagrangian reference frame.

In FIG. 2 , the workpiece 200 gives off optical radiation 201 due toheating by the heat source 202 and the optical sensor is a non-contactpyrometer 203. This pyrometer collects a thermal signal 204. It isassume that the size of the heat source 202 is smaller than the size ofthe field of view of the pyrometer. The thermal signal comprises twotype of features: a slower moving signal 205 that is associated with theheat source 202 gradually coming into the field of the view of thepyrometer as well as representing the background temperature of theworkpiece 200; and a faster moving signal 206 that represents individualhigh temperature excursions caused by the heat source 202 as it movesinto and out of the field of view of the pyrometer.

In FIG. 3 , the raw pyrometer signals 300 are then transformed byextracting features from this data. There are several types featuresthat can be extracted. First, from the slower varying data 301 theheating rate 302 and cooling rate 303 as well as the peak backgroundtemperature 304 can be extracted. Secondly, from the faster varying data305, the heating rate 306, the cooling rate 307 and the peak temperature308 can be derived. The slower varying data features indicated by 302,303, and 304 correspond to the material response, as it is largelydictated by the local thermal boundary conditions such as the thermalconductivity, heat sinking properties, etc. Additionally, the slowervarying thermal data indicates the background temperature of theworkpiece in between scan events from the moving heat source. This againis related to the material response as opposed to the thermal input fromthe process. The more rapidly varying data features indicated by 306,307, and 307 are more representative of the process inputs and theenergy inputs to the manufacturing process on account of the scanningenergy source.

In FIG. 4 , the optical sensor that is capturing the optical emissionsfrom the radiation process is a spectrometer 400 which results in theraw spectrum 401 showing integrated spectral intensity as a function ofwavelength. In general any spectrum will have both background blackbodyradiation features 402 as well as characteristic radiation features 403.It is possible to extract features from raw spectrum by taking the FFTand examining the peak heights in the FFT at some intermediate values ofthe inverse wavelength. For example the region of more rapid change inthe wavelength domain peak 404 can correspond to a local maximum in theFFT 405 in the inverse wavelength domain. The slower the rise of thewavelength domain peak 404 would then correspond to a lower value of thecorresponding FFT peak 405. This particular example could happen forexample when the wavelength domain peak is caused by evaporation ofgiven chemical species. The lower this evaporation, the lower thespectral intensity and the slower the rise towards the spectral peakbecause of the nature of the Gaussian fit of the spectral peak. This forexample could happen when the energy coupling to the workpiece isoptimal in the sense that energy is being absorbed, there is a stableliquid melt pool, and there is sufficient superheat to overcome thelatent heat of melting for new powders being sintered but not so muchsuperheat so as to cause excessive vaporization.

Irrespective of how the features are derived, whether they are from thethermal sensor or the spectrometer, the classification scheme can be thesame. First, the features associated with a baseline condition areidentified as one set of data. Then the features from any given testcase can be compared to the baseline condition as follows. First thefeatures from the baseline case are averaged and a vector of the mean ofthese features M is created. he test vector X has the samedimensionality as the vector of feature means because it has the samenumber of features, which will be also called the degrees of freedom.Then a classification scheme as taught in this present inventioninvolves the use of the Mahalanobis distance, which is simply given by:MD ²=[ X−M ]^(T) ·COV _(X)·[ X−M ]  (5)

Where COV_(X) is the covariance matrix of X. It can be shown that whenthe features are normally distributed, then the square of the MDdistance will be Chi-Square distributed. The Chi Squared probabilitydensity distribution is given by:

$\begin{matrix}{{f\left( {x;k} \right)} = \left\{ \begin{matrix}{\frac{x^{{({k\text{/}2})} - 1}e^{{- x}\text{/}2}}{2^{k\text{/}2}{\Gamma\left( \frac{k}{2} \right)}},} & {{{x \geq 0};}\mspace{45mu}} \\{{0,}\mspace{115mu}} & {{otherwise}.}\end{matrix} \right.} & (6)\end{matrix}$

Where

is the Gamma Function and k is the number of degrees of freedom, whichin this case is identical to the number of features. The critical valueof the Chi-Squared distribution at a given confidence level and a givennumber of degrees of freedom can be calculated. This is a thresholdvalue of the distribution above which a point could be considered as anoutlier within the context of fitting the MD Distance t a Chi-Squareddistribution. For example, at a 95% confidence level, or a criticalp-value of 0.05, the corresponding table of critical values of theChi-Squared distribution and therefore the MD distance squared as wellare given by the following table:

Degrees of Freedom Critical Value of the (also the number Chi-SquaredDistribution - also of Features in critical value of the square of thethe Feature Vector) MD distance 1 3.84 2 5.99 3 7.82 4 9.49 5 11.07 612.59 7 14.07 8 15.51 9 16.92 10 18.31

The present invention provides a method of utilizing optical datathrough a variety of sensors as well as a variety of feature extractiontechniques to enable the classification of nominal vs. off-nominalconditions found in a variety of manufacturing processes that involvethe application of heat by a high energy or high temperature transientheat source.

The present invention has been described in the context of variousexample embodiments. It will be understood that the above description ismerely illustrative of the applications of the principles of the presentinvention, the scope of which is to be determined by the claims viewedin light of the specification. Other variants and modifications of theinvention will be apparent to those of skill in the art.

What is claimed is:
 1. An additive manufacturing system comprising: apowder bed arranged to hold a workpiece; an energy beam arranged togenerate a molten region at the workpiece; a sensor arranged to collectdata related to the molten region; and a processor adapted to determinea coupling efficiency of the energy beam to the workpiece based on thedata.
 2. The additive manufacturing system of claim 1 wherein the energybeam is a laser beam.
 3. The additive manufacturing system of claim 1wherein the powder bed comprises a layer of powder that is selectivelyfused to the workpiece.
 4. The additive manufacturing system of claim 1wherein the sensor is an optical pyrometer.
 5. The additivemanufacturing system of claim 1 wherein the energy beam moves relativeto the workpiece and the sensor has a field of view that moves with theenergy beam.
 6. The additive manufacturing system of claim 1 wherein theenergy beam moves relative to the workpiece and the sensor has a fieldof view that remains stationary.
 7. The additive manufacturing system ofclaim 1 wherein the sensor has a field of view at the powder bed that islarger than a size of the molten region.
 8. The additive manufacturingsystem of claim 1 wherein the sensor is arranged to detect opticalradiation emitted from the molten region.
 9. The additive manufacturingsystem of claim 8 wherein the processor analyzes the data using a fastfourier transfer (FFT) function to generate transformed data.
 10. Theadditive manufacturing system of claim 9 wherein the processordetermines the coupling efficiency from the transformed data.
 11. Amethod comprising: generating an energy beam; directing the energy beamat a workpiece to create a molten region at the workpiece; acquiringdata from a sensor arranged to collect input related to the moltenregion; and calculating a coupling efficiency of the energy beam to theworkpiece based on the data.
 12. The method of claim 11 wherein theenergy beam is a laser beam.
 13. The method of claim 11 furthercomprising a powder bed that includes a layer of powder that isselectively fused to the workpiece.
 14. The method of claim 11 whereinthe sensor is an optical pyrometer.
 15. The method of claim 11 whereinthe energy beam moves relative to the workpiece and the sensor has afield of view that moves with the energy beam.
 16. The method of claim11 wherein the energy beam moves relative to the workpiece and thesensor has a field of view that remains stationary.
 17. The method ofclaim 11 wherein the sensor has a field of view at the workpiece that islarger than a size of the molten region.
 18. The method of claim 11wherein the sensor is arranged to detect optical radiation emitted fromthe molten region.
 19. The method of claim 18 wherein the calculatingcomprises analyzing the data using a fast fourier transfer (FFT)function to generate transformed data.
 20. The method of claim 19wherein the coupling efficiency is determined from the transformed data.